MimicBot achieves state-of-the-art performance on FFAI thanks to several innovations.
First, inspired by recent work on Geometric Deep-Learning [1]}, we observe that the game is characterized by local equivariances and global invariants.
An example of equivariance is the block action and how the local support from friendly and enemy pieces is given.
We designed the policy network to support this effectively, by making use of a specific spatial processing path, which is then controlled by a channel attention mechanism.
Additional future work may comprise the addition of equivariant convolutions in order to fully leverage local symmetries [2]}.
The engineering of new agents for given environments can greatly benefit from the understanding of such symmetries, which can then leveraged in the architecture design. Such design decisions are at the core of several award winning agents [3]}, [4]}.
